Fourier principles for emotion-based human figure animation
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
GI '96 Proceedings of the conference on Graphics interface '96
Designing Sociable Robots
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Studies on gesture expressivity for a virtual agent
Speech Communication
Changing musical emotion: A computational rule system for modifying score and performance
Computer Music Journal
Recognition of affect based on gait patterns
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Scikit-learn: Machine Learning in Python
The Journal of Machine Learning Research
Score-Independent Audio Features for Description of Music Expression
IEEE Transactions on Audio, Speech, and Language Processing
Human behavior understanding for robotics
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
| Hi-index | 0.00 |
Robots that can recognize emotions can improve humans' mental health by providing empathy and social communication. Emotion recognition by robots is challenging because unlike in human-computer environments, facial information is not always available. Instead, our method proposes using speech and gait analysis to recognize human emotion. Previous research suggests that the dynamics of emotional human speech also underlie emotional gait (walking). We investigate the possibility of combining these two modalities via perceptually common parameters: Speed, Intensity, irRegularity, and Extent (SIRE). We map low-level features to this 4D cross-modal emotion space and train a Gaussian Mixture Model using independent samples from both voice and gait. Our results show that a single, modality-mixed trained model can perform emotion recognition for both modalities. Most interestingly, recognition of emotion in gait using a model trained uniquely on speech data gives comparable results to a model trained on gait data alone, providing evidence for a common underlying model for emotion across modalities.